Using Ensemble of Multiple Fine-Tuned EfficientNet Models for Skin Cancer Classification

• Published in: 2023 3rd Asian Conference on Innovation in Technology (ASIANCON) pp. 1 - 4
• Main Authors: Joshi, Karan P., Davaria, Sneh, Saxena, Kumkum
• Format: Conference Proceeding
• Language: English
• Published: IEEE 25-08-2023
• Subjects: Data augmentation; Data models; Deep architecture; Dermatology; efficientnet; ensemble; Ensemble learning; fine-tuning; Measurement; skin cancer; Technological innovation; transfer learning
• DOI: 10.1109/ASIANCON58793.2023.10270209

Skin cancer is a prevalent form of cancer, and its early and accurate identification is critical for effective treatment. In this research paper, using an ensemble of fine-tuned EfficientNet models we proposed an improved approach for skin cancer classification. Our methodology incorporates data augmentation techniques to augment the dataset size, fine-tuning of the EfficientNet model by unfreezing the last few blocks, and employing an average ensemble for enhanced classification accuracy. The proposed approach when compared with other related work proved its effectiveness by outperforming them. Furthermore, our proposed ensemble method shows a precision value of 0.990, and accuracy of 0.988. Our findings demonstrate the effectiveness of the proposed methodology and its potential to significantly improve the diagnosis and treatment of skin cancer.